ABSTRACT The prevalence of obesity in the United States has risen to record levels over the past 40 years, putting strain on the healthcare system and creating difficult challenges for medical imaging. We propose to overcome the challenges that obesity poses to ultrasound imaging by (1) developing novel image-quality improvement techniques, and (2) implementing them on pulse-echo ultrasound imaging systems to yield high-quality images of the liver. Ultrasound imaging is uniquely affected by the presence of additional connective tissue and thick subcutaneous fat layers in overweight and obese patients; these additional subcutaneous layers greatly exacerbate reverberation and phase-aberration of the acoustic wave, leading to high levels of clutter, degraded resolution, and overall poor-quality ultrasound images. Our proposed methods will determine the local speed-of-sound in abdominal tissue layers and use this information to accomplish distributed phase-aberration correction. We also apply machine learning techniques to model and suppress the effects of reverberation clutter and speckle noise. The combination of these techniques is expected to achieve significant improvements in liver image quality. These image-quality improvement methods will be implemented on a real-time ultrasound scanner and will be evaluated in clinical imaging tasks of overweight and obese patients undergoing ultrasound surveillance of hepatocellular carcinoma. Successful development of the proposed technology will not only enable high-quality ultrasound imaging of the liver in otherwise difficult-to-image overweight and obese patients, but also facilitate improved image quality across nearly all ultrasound imaging applications, for all populations.